




"""simple docstring"""


import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


A_         =							{
    '''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    '''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    '''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
    '''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def    UpperCAmelCase__			(snake_case__				:   Union[str, Any]       ):
  """simple docstring"""

  assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
  assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
  if args.mlm:
    assert os.path.isfile(args.token_counts       )
    assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
  else:
    assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

  assert args.teacher_type == args.student_type or (
      args.student_type == "distilbert" and args.teacher_type == "bert"
  )
  assert os.path.isfile(args.student_config       )
  if args.student_pretrained_weights is not None:
    assert os.path.isfile(args.student_pretrained_weights       )

  if args.freeze_token_type_embds:
    assert args.student_type in ["roberta"]

  assert args.alpha_ce >= 0.0
  assert args.alpha_mlm >= 0.0
  assert args.alpha_clm >= 0.0
  assert args.alpha_mse >= 0.0
  assert args.alpha_cos >= 0.0
  assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def    UpperCAmelCase__			(snake_case__				:   str						,  snake_case__				:   Optional[Any]       ):
  """simple docstring"""

  if args.student_type == "roberta":
    _snake_case				:						Union[str, Any]								=    False
  elif args.student_type == "gpt2":
    _snake_case				:						List[Any]								=    False


def    UpperCAmelCase__			(snake_case__				:   List[str]						,  snake_case__				:   List[str]       ):
  """simple docstring"""

  if args.student_type == "roberta":
    _snake_case				:						Dict								=    False


def    UpperCAmelCase__			():
  """simple docstring"""

  _snake_case				:						int								=    argparse.ArgumentParser(description="""Training"""       )
  parser.add_argument("""--force"""						,  action="""store_true"""						,  help="""Overwrite dump_path if it already exists."""       )

  parser.add_argument(
      """--dump_path"""						,  type=UpperCamelCase_						,  required=UpperCamelCase_						,  help="""The output directory (log, checkpoints, parameters, etc.)"""       )
  parser.add_argument(
      """--data_file"""						,  type=UpperCamelCase_						,  required=UpperCamelCase_						,  help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence."""						,  )

  parser.add_argument(
      """--student_type"""						,  type=UpperCamelCase_						,  choices=["""distilbert""", """roberta""", """gpt2"""]						,  required=UpperCamelCase_						,  help="""The student type (DistilBERT, RoBERTa)."""						,  )
  parser.add_argument("""--student_config"""						,  type=UpperCamelCase_						,  required=UpperCamelCase_						,  help="""Path to the student configuration."""       )
  parser.add_argument(
      """--student_pretrained_weights"""						,  default=UpperCamelCase_						,  type=UpperCamelCase_						,  help="""Load student initialization checkpoint."""       )

  parser.add_argument(
      """--teacher_type"""						,  choices=["""bert""", """roberta""", """gpt2"""]						,  required=UpperCamelCase_						,  help="""Teacher type (BERT, RoBERTa)."""       )
  parser.add_argument("""--teacher_name"""						,  type=UpperCamelCase_						,  required=UpperCamelCase_						,  help="""The teacher model."""       )

  parser.add_argument("""--temperature"""						,  default=2.0						,  type=UpperCamelCase_						,  help="""Temperature for the softmax temperature."""       )
  parser.add_argument(
      """--alpha_ce"""						,  default=0.5						,  type=UpperCamelCase_						,  help="""Linear weight for the distillation loss. Must be >=0."""       )
  parser.add_argument(
      """--alpha_mlm"""						,  default=0.0						,  type=UpperCamelCase_						,  help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."""						,  )
  parser.add_argument("""--alpha_clm"""						,  default=0.5						,  type=UpperCamelCase_						,  help="""Linear weight for the CLM loss. Must be >=0."""       )
  parser.add_argument("""--alpha_mse"""						,  default=0.0						,  type=UpperCamelCase_						,  help="""Linear weight of the MSE loss. Must be >=0."""       )
  parser.add_argument(
      """--alpha_cos"""						,  default=0.0						,  type=UpperCamelCase_						,  help="""Linear weight of the cosine embedding loss. Must be >=0."""       )

  parser.add_argument(
      """--mlm"""						,  action="""store_true"""						,  help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."""       )
  parser.add_argument(
      """--mlm_mask_prop"""						,  default=0.15						,  type=UpperCamelCase_						,  help="""Proportion of tokens for which we need to make a prediction."""						,  )
  parser.add_argument("""--word_mask"""						,  default=0.8						,  type=UpperCamelCase_						,  help="""Proportion of tokens to mask out."""       )
  parser.add_argument("""--word_keep"""						,  default=0.1						,  type=UpperCamelCase_						,  help="""Proportion of tokens to keep."""       )
  parser.add_argument("""--word_rand"""						,  default=0.1						,  type=UpperCamelCase_						,  help="""Proportion of tokens to randomly replace."""       )
  parser.add_argument(
      """--mlm_smoothing"""						,  default=0.7						,  type=UpperCamelCase_						,  help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."""						,  )
  parser.add_argument("""--token_counts"""						,  type=UpperCamelCase_						,  help="""The token counts in the data_file for MLM."""       )

  parser.add_argument(
      """--restrict_ce_to_mask"""						,  action="""store_true"""						,  help="""If true, compute the distillation loss only the [MLM] prediction distribution."""						,  )
  parser.add_argument(
      """--freeze_pos_embs"""						,  action="""store_true"""						,  help="""Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only."""						,  )
  parser.add_argument(
      """--freeze_token_type_embds"""						,  action="""store_true"""						,  help="""Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only."""						,  )

  parser.add_argument("""--n_epoch"""						,  type=UpperCamelCase_						,  default=3						,  help="""Number of pass on the whole dataset."""       )
  parser.add_argument("""--batch_size"""						,  type=UpperCamelCase_						,  default=5						,  help="""Batch size (for each process)."""       )
  parser.add_argument(
      """--group_by_size"""						,  action="""store_false"""						,  help="""If true, group sequences that have similar length into the same batch. Default is true."""						,  )

  parser.add_argument(
      """--gradient_accumulation_steps"""						,  type=UpperCamelCase_						,  default=50						,  help="""Gradient accumulation for larger training batches."""						,  )
  parser.add_argument("""--warmup_prop"""						,  default=0.05						,  type=UpperCamelCase_						,  help="""Linear warmup proportion."""       )
  parser.add_argument("""--weight_decay"""						,  default=0.0						,  type=UpperCamelCase_						,  help="""Weight decay if we apply some."""       )
  parser.add_argument("""--learning_rate"""						,  default=5e-4						,  type=UpperCamelCase_						,  help="""The initial learning rate for Adam."""       )
  parser.add_argument("""--adam_epsilon"""						,  default=1e-6						,  type=UpperCamelCase_						,  help="""Epsilon for Adam optimizer."""       )
  parser.add_argument("""--max_grad_norm"""						,  default=5.0						,  type=UpperCamelCase_						,  help="""Max gradient norm."""       )
  parser.add_argument("""--initializer_range"""						,  default=0.02						,  type=UpperCamelCase_						,  help="""Random initialization range."""       )

  parser.add_argument(
      """--fp16"""						,  action="""store_true"""						,  help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""						,  )
  parser.add_argument(
      """--fp16_opt_level"""						,  type=UpperCamelCase_						,  default="""O1"""						,  help=(
          """For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']."""
          """See details at https://nvidia.github.io/apex/amp.html"""
      )						,  )
  parser.add_argument("""--n_gpu"""						,  type=UpperCamelCase_						,  default=1						,  help="""Number of GPUs in the node."""       )
  parser.add_argument("""--local_rank"""						,  type=UpperCamelCase_						,  default=-1						,  help="""Distributed training - Local rank"""       )
  parser.add_argument("""--seed"""						,  type=UpperCamelCase_						,  default=56						,  help="""Random seed"""       )

  parser.add_argument("""--log_interval"""						,  type=UpperCamelCase_						,  default=5_00						,  help="""Tensorboard logging interval."""       )
  parser.add_argument("""--checkpoint_interval"""						,  type=UpperCamelCase_						,  default=40_00						,  help="""Checkpoint interval."""       )
  _snake_case				:						Union[str, Any]								=    parser.parse_args()
  sanity_checks(UpperCamelCase_       )

  # ARGS #
  init_gpu_params(UpperCamelCase_       )
  set_seed(UpperCamelCase_       )
  if args.is_master:
    if os.path.exists(args.dump_path       ):
      if not args.force:
        raise ValueError(
            F"Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"
            """ itUse `--force` if you want to overwrite it"""       )
      else:
        shutil.rmtree(args.dump_path       )

    if not os.path.exists(args.dump_path       ):
      os.makedirs(args.dump_path       )
    logger.info(F"Experiment will be dumped and logged in {args.dump_path}"       )

    # SAVE PARAMS #
    logger.info(F"Param: {args}"       )
    with open(os.path.join(args.dump_path						,  """parameters.json"""       )						,  """w"""       ) as f:
      json.dump(vars(UpperCamelCase_       )						,  UpperCamelCase_						,  indent=4       )
    git_log(args.dump_path       )

  _snake_case						,   _snake_case						,   _snake_case				:						Optional[int]								=    MODEL_CLASSES[args.student_type]
  _snake_case						,   _snake_case						,   _snake_case				:						str								=    MODEL_CLASSES[args.teacher_type]

  # TOKENIZER #
  _snake_case				:						str								=    teacher_tokenizer_class.from_pretrained(args.teacher_name       )
  _snake_case				:						Optional[Any]								=    {}
  for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
    _snake_case				:						List[str]								=    tokenizer.all_special_tokens.index(UpperCamelCase_       )
    _snake_case				:						Dict								=    tokenizer.all_special_ids[idx]
  logger.info(F"Special tokens {special_tok_ids}"       )
  _snake_case				:						int								=    special_tok_ids
  _snake_case				:						Any								=    tokenizer.max_model_input_sizes[args.teacher_name]

  # DATA LOADER #
  logger.info(F"Loading data from {args.data_file}"       )
  with open(args.data_file						,  """rb"""       ) as fp:
    _snake_case				:						List[Any]								=    pickle.load(UpperCamelCase_       )

  if args.mlm:
    logger.info(F"Loading token counts from {args.token_counts} (already pre-computed)"       )
    with open(args.token_counts						,  """rb"""       ) as fp:
      _snake_case				:						Optional[Any]								=    pickle.load(UpperCamelCase_       )

    _snake_case				:						str								=    np.maximum(UpperCamelCase_						,  1       ) ** -args.mlm_smoothing
    for idx in special_tok_ids.values():
      _snake_case				:						Union[str, Any]								=    0.0  # do not predict special tokens
    _snake_case				:						Tuple								=    torch.from_numpy(UpperCamelCase_       )
  else:
    _snake_case				:						Union[str, Any]								=    None

  _snake_case				:						Optional[Any]								=    LmSeqsDataset(params=UpperCamelCase_						,  data=UpperCamelCase_       )
  logger.info("""Data loader created."""       )

  # STUDENT #
  logger.info(F"Loading student config from {args.student_config}"       )
  _snake_case				:						Dict								=    student_config_class.from_pretrained(args.student_config       )
  _snake_case				:						Dict								=    True

  if args.student_pretrained_weights is not None:
    logger.info(F"Loading pretrained weights from {args.student_pretrained_weights}"       )
    _snake_case				:						Dict								=    student_model_class.from_pretrained(args.student_pretrained_weights						,  config=UpperCamelCase_       )
  else:
    _snake_case				:						Tuple								=    student_model_class(UpperCamelCase_       )

  if args.n_gpu > 0:
    student.to(F"cuda:{args.local_rank}"       )
  logger.info("""Student loaded."""       )

  # TEACHER #
  _snake_case				:						List[Any]								=    teacher_model_class.from_pretrained(args.teacher_name						,  output_hidden_states=UpperCamelCase_       )
  if args.n_gpu > 0:
    teacher.to(F"cuda:{args.local_rank}"       )
  logger.info(F"Teacher loaded from {args.teacher_name}."       )

  # FREEZING #
  if args.freeze_pos_embs:
    freeze_pos_embeddings(UpperCamelCase_						,  UpperCamelCase_       )
  if args.freeze_token_type_embds:
    freeze_token_type_embeddings(UpperCamelCase_						,  UpperCamelCase_       )

  # SANITY CHECKS #
  assert student.config.vocab_size == teacher.config.vocab_size
  assert student.config.hidden_size == teacher.config.hidden_size
  assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
  if args.mlm:
    assert token_probs.size(0       ) == stu_architecture_config.vocab_size

  # DISTILLER #
  torch.cuda.empty_cache()
  _snake_case				:						int								=    Distiller(
      params=UpperCamelCase_						,  dataset=UpperCamelCase_						,  token_probs=UpperCamelCase_						,  student=UpperCamelCase_						,  teacher=UpperCamelCase_       )
  distiller.train()
  logger.info("""Let\'s go get some drinks."""       )


if __name__ == "__main__":
     main()







'''simple docstring'''

from __future__ import annotations






def 				a  (     UpperCamelCase_    :    list[int]      ,					UpperCamelCase_    :    list[int]      ,					UpperCamelCase_    :    int			)       ->	tuple[float, list[float]]:
		snake_case__	     =list(range(len(UpperCamelCase_			)			)			)
		snake_case__	     =[v / w for v, w in zip(UpperCamelCase_      ,					UpperCamelCase_			)]
		index.sort(key=lambda UpperCamelCase_			: ratio[i]      ,					reverse=UpperCamelCase_			)

		snake_case__	     =0
		snake_case__	     =[0] * len(UpperCamelCase_			)
		for i in index:
				if weight[i] <= capacity:
						snake_case__	     =1
						max_value += value[i]
						capacity -= weight[i]
				else:
						snake_case__	     =capacity / weight[i]
						max_value += value[i] * capacity / weight[i]
						break

		return max_value, fractions


if __name__ == "__main__":
			import doctest

			doctest.testmod()




